The escalating prevalence of diabetes globally underscores the need for diabetes management. Recent research highlights the growing focus on digital biomarkers in diabetes management, with innovations in computational frameworks and noninvasive monitoring techniques using personalized glucose metrics. However, they predominantly focus on insulin dosing and specific glucose values, or with limited attention given to overall glycemic control. This leaves a gap in expanding the scope of digital biomarkers for overall glycemic control in diabetes management. To address such a research gap, we propose GluMarker -- an end-to-end framework for modeling digital biomarkers using broader factors sources to predict glycemic control. Through the assessment and refinement of various machine learning baselines, GluMarker achieves state-of-the-art on Anderson's dataset in predicting next-day glycemic control. Moreover, our research identifies key digital biomarkers for the next day's glycemic control prediction. These identified biomarkers are instrumental in illuminating the daily factors that influence glycemic management, offering vital insights for diabetes care.

提出了一种名为GluMarker的综合框架，用于建模数字生物标志物，以预测糖尿病患者的血糖控制情况，通过评估和改进各种机器学习基线，在预测第二天的血糖控制方面达到了最先进的水平，并鉴定出了对血糖控制具有重要影响的数字生物标志物，为糖尿病护理提供关键洞察。

GluMarker：一种通过数字生物标志物对血糖控制进行预测的新型建模方法